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Speaker adaptation using relevance vector regression for HMM-based expressive TTS
The conventional maximum likelihood linear regression (MLLR)-based adaptation algorithm employed to acoustic hidden Markov models (HMMs) is too restricted in linear regression to represent the details of mapping charateristics. To overcome this problem, we propose the relevance vector regression (RVR)-based model parameter adaptation technique. In this framework, the conventional technique is extended to have much more basis functions. Also, the weights for conducting a transform matrix aredoi:10.21437/interspeech.2015-307 fatcat:3rgndvj34rburhjrzbjuburewq